Abstract

Selecting and estimating parsimonious models is often desired, but hard to achieve. This is particularly true when models can potentially contain a very large number of parameters but data are scarce-as is the case for many macro-economic models in general and interest-rate models in particular. These models need to cater for a large number of potential relationships and dependencies, but are fitted on low-frequency data to focus on the bigger picture and long-term effects. To identify the ideal model and estimating it is then particularly demanding from an optimization perspective. In this paper, we suggest an evolutionary approach that considers model selection and estimation simultaneously. Numerical experiments with artificial data suggest that the approach is well suited for this type of problem. In an empirical application for short-term and long-term interest rates denominated in US dollar, euro and the Japanese yen, respectively, parsimonious model structures are identified that highlight the dependencies as well as spill-overs across maturities and currencies.